Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Breast cancer has been one of the most challenging women’s cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.